When the Past Shapes Action: Understanding Embodied Memory

The paper "When the Past Shapes Action: Understanding Embodied Memory" surveys how embodied AI agents can preserve and use past observations, actions, and experiences to improve decision-making in dynamic environments. It categorizes existing approaches along six memory requirements—world-state persistence, task progress, belief maintenance, experiential learning, etc.—and eight architectural styles such as recurrent states, fast weights, and spatial memory. The authors evaluate how current benchmarks test memory-dependent behavior and highlight open challenges in selecting relevant experiences, maintaining consistent beliefs, and consolidating knowledge across episodes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• agents
• robotics
• benchmarks